Cloud-Native SRE: Scale AI Clusters & Ray Platform

Carbon Data Solutions; United States; À distance; USD 140 000 - 190 000; Plein temps

Résumé du poste

Anyscale is seeking a Site Reliability Engineer to join the Infra team. You will help build the scalable backbone for distributed AI workloads in the cloud, designing both control and data plane components and optimizing scheduling across cloud and on-prem environments.

You will work on open-source Ray, integrate with our proprietary platform, and collaborate with ML and systems experts to push AI infrastructure forward.

Qualifications

• Bachelor's degree in CS, Eng, or equivalent.
• 3+ years of production code experience.
• Experience building highly available distributed systems.
• Expertise in cloud-native tech and Kubernetes deployments.
• Strong knowledge of networking, security, and authentication in cloud environments.

Responsabilités

• Design, build, and scale services that orchestrate Ray clusters across cloud and on-prem environments.
• Optimize control plane components for large-scale distributed AI/ML workloads.
• Build intelligent scheduling and resource management systems for heterogeneous compute clusters.
• Develop features to enhance reliability, performance, scalability, and observability of Ray workloads.
• Support and optimize accelerator integration (e.g., GPUs, TPUs).
• Handle container image management and dependency resolution for distributed workloads.
• Participate in code reviews, design and architecture discussions.
• Provide on-call support and collaborate with customer and field teams to troubleshoot infrastructure issues.
• Collaborate with distributed systems and ML experts to push AI infrastructure boundaries.

Connaissances

Go

Python

Distributed systems

Networking

Formation

Bachelor's degree in Computer Science, Engineering, or equivalent

Outils

Kubernetes

Prometheus

Grafana

AWS

Azure

GCP

Description du poste

Anyscale is seeking a Site Reliability Engineer to join the Infra team. You will help build the scalable backbone for distributed AI workloads in the cloud, designing both control and data plane components and optimizing scheduling across cloud and on-prem environments.

You will work on open-source Ray, integrate with our proprietary platform, and collaborate with ML and systems experts to push AI infrastructure forward.